2015.07.30: resources

Talk

stevejbrown's list of data science resources (see below).

Hands-on

Titanic tutorial on kaggle.com

Data science resources

Python

Books

1. Python for Data Analysis by Wes McKinney
2. Data Science from Scratch

Tools

1. Anaconda and the conda package manager (includes ipython notebook and spyder)
2. Ipython (Jupyter) notebook
3. Spyder

R

Books

1. R in Action by Robert Kabacoff
2. Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani
3. Elements of Statistical Learning by Hastie, Tibshirani, and Friedman

Blogs

1. R-bloggers

Tools

1. RStudio

Language agnostic

Books

Statistics

1. All of Statistics by Larry Wasserman [Intermediate]
2. Statistics by Freedman, Pisani, and Purves [Beginner]

Data visualization

1. The Visual Display of Quantitative Information by Edward Tufte

Popular data science

1. [The Signal and the Noise by Nate Silver] (http://www.amazon.com/The-Signal-Noise-Predictions-Fail-but/dp/0143125087)

Podcasts

1. Partially Derivative
2. The Talking Machines
3. O'Reilly Data Show

Websites

1. Datatau
2. r/datascience
3. Datascienceweekly
4. kaggle

Courses

1. Andrew Ng's machine learning course
2. John Hopkins data specialization on Coursera (courses can be taken for free)

Meetups

1. SB data science meetup
2. SB R users group
3. SB machine learning meetup

Another list

1. Data science masters